My AI Stopped "Guessing" and Started "Thinking": Implementing a Planning & Reasoning Architecture
In previous articles, I talked about how I generate tests using LLMs, parse Swagger schemas, and fight against hardcoded data. But "naked" LLM generation has a fundamental problem: it is linear. The model often tries to guess the next step without understanding the big picture.
Yesterday, I deployed the biggest architectural update since I started development — the System of Planning and Reasoning.
Now, Debuggo doesn't just "write code." It acts like a Senior QA: first, it analyzes requirements, assesses risks, decomposes the task into subtasks, and only then begins to act.
I want to show you "under the hood" how this works and, most importantly, honestly compare: did it actually get faster?
The Problem: Why Does AI Get Lost?
Previously, if I asked: "Create a group, add a user to it, verif…
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